Paper Title
Efficient RAG Framework for Large-Scale Knowledge Bases
Article Identifiers
: Keywords
LLM (Large Language Model), RAG Model, Knowledge Distillation, Quantization and Pruning Techniques, NLP (Natural Language Processing)
Abstract
This research paper explores the nuances of optimizing large models of languages (LLMs) for the effective creation and retrieval of information. The current research investigation focuses on two main approaches: Knowledge Distillation (KD) and Retrieval-Augmented Generation (RAG), in addition to quantization and pruning strategies. KD reduces the size of LLMs without compromising functionality to maximize LLM efficiency and resource usage, whereas RAG combines external knowledge sources with LLMs to allow contextually relevant replies. LLMs are further optimized for constrained resource contexts through the use of quantization and trimming algorithms. By conducting a thorough assessment of the querying procedure, the research demonstrates the capacity of the model to produce precise answers and pinpoint areas in need of improvement. This study advances the architecture of the RAG Framework. It investigates its possibilities, providing large-scale scalable knowledge and practical solutions for knowledge creation and retrieval across a variety of fields, so opening the door for improved information access and human-machine interaction. This research will support the advancement of knowledge retrieval in all fields in the future.
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How To Cite (APA)
Karthik Meduri, Geeta Sandeep Nadella, Hari Gonaygunta, Mohan Harish Maturi, & Farheen Fatima (April-2024). Efficient RAG Framework for Large-Scale Knowledge Bases. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), h613-h622. https://ijnrd.org/papers/IJNRD2404764.pdf
Issue
Volume 9 Issue 4, April-2024
Pages : h613-h622
Other Publication Details
Paper Reg. ID: IJNRD_219784
Published Paper Id: IJNRD2404764
Downloads: 000122254
Research Area: Computer Science & TechnologyÂ
Author Type: Foreign Author
Country: Stockton, California, United States
Published Paper PDF: https://ijnrd.org/papers/IJNRD2404764.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404764
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